Adaptive Musical Instruments (AMIs): Past, Present, and Future Research Directions
Bibliographic record
Abstract
We review and discuss the literature related to adaptive musical instruments since 2000, focusing on the use of such instruments with children with disabilities. The aim of this review is to provide a synthesis of perspectives and answer the following questions: How have music technologies, including both software and hardware, been used for children with disabilities and how have they been tested and evaluated? What have been the research questions asked and outcomes evaluated concerning these instruments? The studies reviewed include intervention, narrative and descriptive studies. One observation is that adaptive instrument design and research cuts across many different disciplines including music therapy, education and engineering. We considered articles taking functional and rehabilitation informed perspectives as well as critical disability studies, for which music making is often discussed as a human right independently of potential benefits. We discuss methodological approaches used in these studies, and reports of user’s opinions concerning the use of AMIs. It is worth noting that most uses of AMIs by the population under consideration are highly improvisatory, and so a methodological challenge frequently reported is how can the effectiveness of AMIs be assessed without focusing only on easily measurable outputs? We reveal divisions existing between research focusing on the use of AMIs with precise therapeutic and pedagogic goals in mind, and that interested in more general positive effects of improvised collective creative activity and its role in community building. With this two-fold perspective, we analyse the limitations of current research and derive questions for future directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".